Overview

Dataset statistics

Number of variables20
Number of observations295516
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory45.1 MiB
Average record size in memory160.0 B

Variable types

Numeric20

Alerts

CO (ppm) is highly overall correlated with Time (s) and 9 other fieldsHigh correlation
Heater voltage (V) is highly overall correlated with R4 (MOhm) and 8 other fieldsHigh correlation
R1 (MOhm) is highly overall correlated with R2 (MOhm) and 7 other fieldsHigh correlation
R2 (MOhm) is highly overall correlated with R1 (MOhm) and 5 other fieldsHigh correlation
R3 (MOhm) is highly overall correlated with R1 (MOhm) and 5 other fieldsHigh correlation
R4 (MOhm) is highly overall correlated with Humidity (%r.h.) and 14 other fieldsHigh correlation
R5 (MOhm) is highly overall correlated with R1 (MOhm) and 12 other fieldsHigh correlation
R6 (MOhm) is highly overall correlated with Heater voltage (V) and 13 other fieldsHigh correlation
R7 (MOhm) is highly overall correlated with R1 (MOhm) and 12 other fieldsHigh correlation
R8 (MOhm) is highly overall correlated with CO (ppm) and 11 other fieldsHigh correlation
R9 (MOhm) is highly overall correlated with CO (ppm) and 12 other fieldsHigh correlation
R10 (MOhm) is highly overall correlated with CO (ppm) and 11 other fieldsHigh correlation
R11 (MOhm) is highly overall correlated with CO (ppm) and 11 other fieldsHigh correlation
R12 (MOhm) is highly overall correlated with CO (ppm) and 11 other fieldsHigh correlation
R13 (MOhm) is highly overall correlated with CO (ppm) and 12 other fieldsHigh correlation
R14 (MOhm) is highly overall correlated with CO (ppm) and 11 other fieldsHigh correlation
Time (s) is highly overall correlated with CO (ppm) and 2 other fieldsHigh correlation
Humidity (%r.h.) is highly overall correlated with Time (s) and 3 other fieldsHigh correlation
Temperature (C) is highly overall correlated with Time (s) and 2 other fieldsHigh correlation
Flow rate (mL/min) is highly skewed (γ1 = -98.05186846)Skewed
Time (s) is uniformly distributedUniform
Time (s) has unique valuesUnique
CO (ppm) has 32192 (10.9%) zerosZeros

Reproduction

Analysis started2022-12-20 08:21:09.754670
Analysis finished2022-12-20 08:25:54.293454
Duration4 minutes and 44.54 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

Time (s)
Real number (ℝ)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct295516
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45450.911
Minimum0
Maximum90909.585
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:55:54.574813image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4550.3965
Q122738.039
median45451.35
Q368153.277
95-th percentile86359.916
Maximum90909.585
Range90909.585
Interquartile range (IQR)45415.239

Descriptive statistics

Standard deviation26235.338
Coefficient of variation (CV)0.57722359
Kurtosis-1.1988754
Mean45450.911
Median Absolute Deviation (MAD)22707.699
Skewness6.9101035 × 10-5
Sum1.3431472 × 1010
Variance6.8829297 × 108
MonotonicityStrictly increasing
2022-12-20T13:55:54.862983image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
60613.283 1
 
< 0.1%
60585.064 1
 
< 0.1%
60584.754 1
 
< 0.1%
60584.445 1
 
< 0.1%
60584.137 1
 
< 0.1%
60583.827 1
 
< 0.1%
60583.518 1
 
< 0.1%
60583.208 1
 
< 0.1%
60582.898 1
 
< 0.1%
Other values (295506) 295506
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
0.309 1
< 0.1%
0.619 1
< 0.1%
0.928 1
< 0.1%
1.235 1
< 0.1%
1.545 1
< 0.1%
1.853 1
< 0.1%
2.161 1
< 0.1%
2.47 1
< 0.1%
2.779 1
< 0.1%
ValueCountFrequency (%)
90909.585 1
< 0.1%
90909.281 1
< 0.1%
90908.976 1
< 0.1%
90908.671 1
< 0.1%
90908.366 1
< 0.1%
90908.061 1
< 0.1%
90907.757 1
< 0.1%
90907.451 1
< 0.1%
90907.146 1
< 0.1%
90906.841 1
< 0.1%

CO (ppm)
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct310
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8999365
Minimum0
Maximum20
Zeros32192
Zeros (%)10.9%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:55:55.029027image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.44
median8.89
Q315.56
95-th percentile20
Maximum20
Range20
Interquartile range (IQR)11.12

Descriptive statistics

Standard deviation6.4290286
Coefficient of variation (CV)0.649401
Kurtosis-1.233963
Mean9.8999365
Median Absolute Deviation (MAD)6.67
Skewness0.0090592728
Sum2925589.6
Variance41.332409
MonotonicityNot monotonic
2022-12-20T13:55:55.188455image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32192
10.9%
17.78 29265
9.9%
4.44 29252
9.9%
13.33 29247
9.9%
6.67 29239
9.9%
20 29231
9.9%
2.22 29220
9.9%
11.11 29219
9.9%
15.56 29203
9.9%
8.89 29147
9.9%
Other values (300) 301
 
0.1%
ValueCountFrequency (%)
0 32192
10.9%
0.0266 1
 
< 0.1%
0.22 1
 
< 0.1%
0.2889 1
 
< 0.1%
0.3378 1
 
< 0.1%
0.4595 1
 
< 0.1%
0.5705 1
 
< 0.1%
0.6222 1
 
< 0.1%
0.6845 1
 
< 0.1%
0.9191 1
 
< 0.1%
ValueCountFrequency (%)
20 29231
9.9%
19.7935 1
 
< 0.1%
19.7467 1
 
< 0.1%
19.32 1
 
< 0.1%
19.1131 1
 
< 0.1%
19.1053 1
 
< 0.1%
18.8 1
 
< 0.1%
18.7879 1
 
< 0.1%
18.787 1
 
< 0.1%
18.7612 1
 
< 0.1%

Humidity (%r.h.)
Real number (ℝ)

Distinct21644
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.748175
Minimum16.39
Maximum72.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:55:55.372603image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum16.39
5-th percentile23.09
Q136.14
median46.65
Q355.33
95-th percentile65.23
Maximum72.92
Range56.53
Interquartile range (IQR)19.19

Descriptive statistics

Standard deviation12.467841
Coefficient of variation (CV)0.27253199
Kurtosis-0.73838758
Mean45.748175
Median Absolute Deviation (MAD)9.45
Skewness-0.16087023
Sum13519318
Variance155.44706
MonotonicityNot monotonic
2022-12-20T13:55:55.522871image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.92 4837
 
1.6%
51.77 4658
 
1.6%
36.14 3868
 
1.3%
38.26 3312
 
1.1%
30.76 3151
 
1.1%
21.99 3115
 
1.1%
47.67 3105
 
1.1%
31.84 3014
 
1.0%
30.74 2860
 
1.0%
46.64 2846
 
1.0%
Other values (21634) 260750
88.2%
ValueCountFrequency (%)
16.39 152
0.1%
16.3904 1
 
< 0.1%
16.3905 1
 
< 0.1%
16.3909 1
 
< 0.1%
16.391 1
 
< 0.1%
16.3914 1
 
< 0.1%
16.3915 1
 
< 0.1%
16.4553 1
 
< 0.1%
16.4771 1
 
< 0.1%
16.5084 1
 
< 0.1%
ValueCountFrequency (%)
72.92 287
0.1%
72.9194 1
 
< 0.1%
72.9188 1
 
< 0.1%
72.9182 1
 
< 0.1%
72.8067 1
 
< 0.1%
72.8034 1
 
< 0.1%
72.663 1
 
< 0.1%
72.6611 1
 
< 0.1%
72.5184 1
 
< 0.1%
72.5183 1
 
< 0.1%

Temperature (C)
Real number (ℝ)

Distinct3755
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.427412
Minimum26.14
Maximum26.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:55:55.693379image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum26.14
5-th percentile26.22
Q126.38
median26.46
Q326.5
95-th percentile26.58
Maximum26.62
Range0.48
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation0.10374167
Coefficient of variation (CV)0.0039255328
Kurtosis-0.23711187
Mean26.427412
Median Absolute Deviation (MAD)0.04
Skewness-0.66855755
Sum7809723
Variance0.010762334
MonotonicityNot monotonic
2022-12-20T13:55:55.887310image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.46 62870
21.3%
26.5 44961
15.2%
26.42 34794
11.8%
26.54 31538
10.7%
26.38 28110
9.5%
26.22 16089
 
5.4%
26.26 15629
 
5.3%
26.3 11912
 
4.0%
26.34 9774
 
3.3%
26.58 9583
 
3.2%
Other values (3745) 30256
10.2%
ValueCountFrequency (%)
26.14 69
< 0.1%
26.1401 10
 
< 0.1%
26.1407 1
 
< 0.1%
26.1409 1
 
< 0.1%
26.1432 1
 
< 0.1%
26.1435 1
 
< 0.1%
26.1448 1
 
< 0.1%
26.1461 1
 
< 0.1%
26.1475 1
 
< 0.1%
26.1477 1
 
< 0.1%
ValueCountFrequency (%)
26.62 5314
1.8%
26.6199 145
 
< 0.1%
26.6198 51
 
< 0.1%
26.6197 2
 
< 0.1%
26.6194 1
 
< 0.1%
26.6193 2
 
< 0.1%
26.6192 1
 
< 0.1%
26.619 1
 
< 0.1%
26.6189 1
 
< 0.1%
26.6188 1
 
< 0.1%

Flow rate (mL/min)
Real number (ℝ)

Distinct11422
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean239.94252
Minimum0
Maximum272.6063
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:55:56.049106image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile239.7525
Q1239.8952
median239.9723
Q3240.0456
95-th percentile240.1832
Maximum272.6063
Range272.6063
Interquartile range (IQR)0.1504

Descriptive statistics

Standard deviation1.9355314
Coefficient of variation (CV)0.0080666461
Kurtosis11142.54
Mean239.94252
Median Absolute Deviation (MAD)0.0752
Skewness-98.051868
Sum70906853
Variance3.7462817
MonotonicityNot monotonic
2022-12-20T13:55:56.216826image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
240.0058 141
 
< 0.1%
239.99 136
 
< 0.1%
239.9545 135
 
< 0.1%
239.9695 135
 
< 0.1%
239.9822 135
 
< 0.1%
239.9928 135
 
< 0.1%
239.9779 134
 
< 0.1%
239.9857 133
 
< 0.1%
239.964 133
 
< 0.1%
239.9629 132
 
< 0.1%
Other values (11412) 294167
99.5%
ValueCountFrequency (%)
0 8
< 0.1%
0.1037 1
 
< 0.1%
0.5429 1
 
< 0.1%
0.982 1
 
< 0.1%
1.4198 1
 
< 0.1%
31.7527 1
 
< 0.1%
47.1536 1
 
< 0.1%
70.8576 1
 
< 0.1%
77.886 1
 
< 0.1%
102.4733 1
 
< 0.1%
ValueCountFrequency (%)
272.6063 1
< 0.1%
265.4839 1
< 0.1%
262.0708 1
< 0.1%
261.8683 1
< 0.1%
260.4217 1
< 0.1%
259.6269 1
< 0.1%
257.9961 1
< 0.1%
256.6632 1
< 0.1%
255.4238 1
< 0.1%
255.3764 1
< 0.1%

Heater voltage (V)
Real number (ℝ)

Distinct1708
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35531938
Minimum0.199
Maximum0.9009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:55:56.399838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.199
5-th percentile0.2
Q10.2
median0.2
Q30.207
95-th percentile0.899
Maximum0.9009
Range0.7019
Interquartile range (IQR)0.007

Descriptive statistics

Standard deviation0.28869355
Coefficient of variation (CV)0.81249029
Kurtosis-0.20738807
Mean0.35531938
Median Absolute Deviation (MAD)0
Skewness1.3365387
Sum105002.56
Variance0.083343965
MonotonicityNot monotonic
2022-12-20T13:55:56.579028image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 162508
55.0%
0.899 26989
 
9.1%
0.201 9575
 
3.2%
0.898 5093
 
1.7%
0.202 2999
 
1.0%
0.2001 2755
 
0.9%
0.2003 2677
 
0.9%
0.2007 2677
 
0.9%
0.2004 2672
 
0.9%
0.2005 2672
 
0.9%
Other values (1698) 74899
25.3%
ValueCountFrequency (%)
0.199 729
0.2%
0.1991 1013
0.3%
0.1992 1076
0.4%
0.1993 948
0.3%
0.1994 997
0.3%
0.1995 1043
0.4%
0.1996 1011
0.3%
0.1997 987
0.3%
0.1998 929
0.3%
0.1999 1053
0.4%
ValueCountFrequency (%)
0.9009 3
 
< 0.1%
0.9008 3
 
< 0.1%
0.9007 1
 
< 0.1%
0.9006 3
 
< 0.1%
0.9005 2
 
< 0.1%
0.9004 2
 
< 0.1%
0.9002 1
 
< 0.1%
0.9001 1
 
< 0.1%
0.9 2444
0.8%
0.8999 1264
0.4%

R1 (MOhm)
Real number (ℝ)

Distinct8513
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.331572
Minimum0.0321
Maximum116.4568
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:55:56.766649image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0321
5-th percentile0.0783
Q10.4113
median1.7516
Q326.1726
95-th percentile66.4837
Maximum116.4568
Range116.4247
Interquartile range (IQR)25.7613

Descriptive statistics

Standard deviation22.707987
Coefficient of variation (CV)1.4811258
Kurtosis0.99517602
Mean15.331572
Median Absolute Deviation (MAD)1.6697
Skewness1.4629259
Sum4530725
Variance515.65267
MonotonicityNot monotonic
2022-12-20T13:55:56.952826image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68.5571 712
 
0.2%
69.1448 687
 
0.2%
69.6423 684
 
0.2%
0.0944 679
 
0.2%
70.2486 673
 
0.2%
68.0747 670
 
0.2%
66.4837 669
 
0.2%
67.0368 664
 
0.2%
0.0948 655
 
0.2%
66.0296 654
 
0.2%
Other values (8503) 288769
97.7%
ValueCountFrequency (%)
0.0321 1
 
< 0.1%
0.0322 1
 
< 0.1%
0.0326 1
 
< 0.1%
0.0327 2
< 0.1%
0.0328 1
 
< 0.1%
0.0331 3
< 0.1%
0.0332 1
 
< 0.1%
0.0333 2
< 0.1%
0.0334 3
< 0.1%
0.0335 1
 
< 0.1%
ValueCountFrequency (%)
116.4568 1
 
< 0.1%
113.4868 2
 
< 0.1%
111.9292 8
 
< 0.1%
110.6632 7
 
< 0.1%
109.181 4
 
< 0.1%
107.9756 10
 
< 0.1%
106.5634 11
 
< 0.1%
105.4143 22
< 0.1%
104.0673 43
< 0.1%
102.9706 43
< 0.1%

R2 (MOhm)
Real number (ℝ)

Distinct8270
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.59054
Minimum0.0559
Maximum144.618
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:55:57.247438image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0559
5-th percentile0.1395
Q10.4865
median1.3908
Q329.5749
95-th percentile76.9383
Maximum144.618
Range144.5621
Interquartile range (IQR)29.0884

Descriptive statistics

Standard deviation26.811283
Coefficient of variation (CV)1.5241876
Kurtosis0.48223647
Mean17.59054
Median Absolute Deviation (MAD)1.2495
Skewness1.3833763
Sum5198285.9
Variance718.8449
MonotonicityNot monotonic
2022-12-20T13:55:57.404004image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78.3034 1054
 
0.4%
79.0683 1048
 
0.4%
79.7171 1042
 
0.4%
76.3332 1005
 
0.3%
80.5097 990
 
0.3%
81.1822 984
 
0.3%
77.677 980
 
0.3%
75.6194 958
 
0.3%
75.0345 950
 
0.3%
76.9383 935
 
0.3%
Other values (8260) 285570
96.6%
ValueCountFrequency (%)
0.0559 1
 
< 0.1%
0.056 1
 
< 0.1%
0.0565 1
 
< 0.1%
0.0567 1
 
< 0.1%
0.0568 1
 
< 0.1%
0.0573 2
< 0.1%
0.0576 2
< 0.1%
0.0579 1
 
< 0.1%
0.058 3
< 0.1%
0.0581 3
< 0.1%
ValueCountFrequency (%)
144.618 1
 
< 0.1%
133.9633 1
 
< 0.1%
124.4448 1
 
< 0.1%
121.0628 2
 
< 0.1%
119.5851 1
 
< 0.1%
117.8584 1
 
< 0.1%
114.818 2
 
< 0.1%
113.4868 6
< 0.1%
111.9292 11
< 0.1%
110.6632 10
< 0.1%

R3 (MOhm)
Real number (ℝ)

Distinct8221
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.473663
Minimum0.054
Maximum175.1666
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:55:57.587590image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.054
5-th percentile0.1121
Q10.5927
median4.2642
Q345.8407
95-th percentile81.51
Maximum175.1666
Range175.1126
Interquartile range (IQR)45.248

Descriptive statistics

Standard deviation28.825388
Coefficient of variation (CV)1.2826297
Kurtosis-0.34684674
Mean22.473663
Median Absolute Deviation (MAD)4.1503
Skewness1.0362009
Sum6641327
Variance830.90297
MonotonicityNot monotonic
2022-12-20T13:55:57.751467image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83.0507 1206
 
0.4%
83.7703 1132
 
0.4%
84.6501 1120
 
0.4%
85.3974 1092
 
0.4%
82.2033 1082
 
0.4%
80.6932 1054
 
0.4%
81.51 1033
 
0.3%
86.3116 1027
 
0.3%
80.0247 1026
 
0.3%
78.592 987
 
0.3%
Other values (8211) 284757
96.4%
ValueCountFrequency (%)
0.054 1
 
< 0.1%
0.0542 1
 
< 0.1%
0.0553 1
 
< 0.1%
0.0563 1
 
< 0.1%
0.0574 1
 
< 0.1%
0.0575 1
 
< 0.1%
0.0578 1
 
< 0.1%
0.058 1
 
< 0.1%
0.0581 2
< 0.1%
0.0584 3
< 0.1%
ValueCountFrequency (%)
175.1666 1
 
< 0.1%
162.3763 1
 
< 0.1%
153.6975 1
 
< 0.1%
146.3314 1
 
< 0.1%
118.8647 2
 
< 0.1%
114.1263 1
 
< 0.1%
112.8031 5
 
< 0.1%
111.2549 11
 
< 0.1%
109.9965 33
< 0.1%
108.5233 46
< 0.1%

R4 (MOhm)
Real number (ℝ)

Distinct7591
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.21222
Minimum0.0392
Maximum80.9098
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:55:58.000433image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0392
5-th percentile0.1021
Q12.0541
median19.455
Q330.6869
95-th percentile46.6306
Maximum80.9098
Range80.8706
Interquartile range (IQR)28.6328

Descriptive statistics

Standard deviation15.841458
Coefficient of variation (CV)0.82455113
Kurtosis-0.68267849
Mean19.21222
Median Absolute Deviation (MAD)13.7414
Skewness0.40052675
Sum5677518.5
Variance250.95179
MonotonicityNot monotonic
2022-12-20T13:55:58.300622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.9473 842
 
0.3%
29.6865 838
 
0.3%
29.2098 836
 
0.3%
32.1616 835
 
0.3%
31.212 831
 
0.3%
30.1786 824
 
0.3%
31.755 816
 
0.3%
29.797 812
 
0.3%
28.1797 809
 
0.3%
30.6869 806
 
0.3%
Other values (7581) 287267
97.2%
ValueCountFrequency (%)
0.0392 1
< 0.1%
0.0396 1
< 0.1%
0.0405 1
< 0.1%
0.0406 1
< 0.1%
0.0411 1
< 0.1%
0.0413 2
< 0.1%
0.0418 1
< 0.1%
0.0419 1
< 0.1%
0.042 1
< 0.1%
0.0421 1
< 0.1%
ValueCountFrequency (%)
80.9098 1
 
< 0.1%
76.8594 1
 
< 0.1%
76.0133 1
 
< 0.1%
75.3221 4
 
< 0.1%
74.5089 9
 
< 0.1%
73.8444 16
< 0.1%
73.0622 20
< 0.1%
72.4229 16
< 0.1%
71.6701 23
< 0.1%
71.0545 33
< 0.1%

R5 (MOhm)
Real number (ℝ)

Distinct7919
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.498183
Minimum0.0482
Maximum163.0324
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:55:58.528850image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0482
5-th percentile0.1151
Q11.8111
median32.8155
Q351.4875
95-th percentile78.1461
Maximum163.0324
Range162.9842
Interquartile range (IQR)49.6764

Descriptive statistics

Standard deviation26.972763
Coefficient of variation (CV)0.85632757
Kurtosis-0.97413369
Mean31.498183
Median Absolute Deviation (MAD)25.1466
Skewness0.34433401
Sum9308217
Variance727.52992
MonotonicityNot monotonic
2022-12-20T13:55:58.708650image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48.0682 1349
 
0.5%
49.1574 1339
 
0.5%
47.5412 1299
 
0.4%
47.2584 1291
 
0.4%
49.4117 1288
 
0.4%
47.7793 1279
 
0.4%
49.7203 1264
 
0.4%
48.3116 1247
 
0.4%
50.562 1243
 
0.4%
48.6068 1240
 
0.4%
Other values (7909) 282677
95.7%
ValueCountFrequency (%)
0.0482 1
< 0.1%
0.0494 1
< 0.1%
0.0495 1
< 0.1%
0.0499 1
< 0.1%
0.0502 1
< 0.1%
0.0505 1
< 0.1%
0.0506 1
< 0.1%
0.0507 2
< 0.1%
0.0508 1
< 0.1%
0.0511 1
< 0.1%
ValueCountFrequency (%)
163.0324 1
 
< 0.1%
151.9291 1
 
< 0.1%
126.116 1
 
< 0.1%
124.1949 4
 
< 0.1%
122.6378 2
 
< 0.1%
120.8197 3
 
< 0.1%
119.345 8
 
< 0.1%
117.6218 11
 
< 0.1%
116.223 9
 
< 0.1%
114.5874 31
< 0.1%

R6 (MOhm)
Real number (ℝ)

Distinct7873
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.963481
Minimum0.0484
Maximum168.4491
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:55:58.956667image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0484
5-th percentile0.1243
Q11.5832
median23.3381
Q349.8802
95-th percentile79.1164
Maximum168.4491
Range168.4007
Interquartile range (IQR)48.297

Descriptive statistics

Standard deviation27.343755
Coefficient of variation (CV)0.94407694
Kurtosis-0.8707065
Mean28.963481
Median Absolute Deviation (MAD)23.19085
Skewness0.5517105
Sum8559172.1
Variance747.68092
MonotonicityNot monotonic
2022-12-20T13:55:59.163219image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49.8802 1156
 
0.4%
48.1206 1150
 
0.4%
78.4368 1148
 
0.4%
47.3106 1148
 
0.4%
81.5165 1142
 
0.4%
48.6935 1135
 
0.4%
77.6364 1133
 
0.4%
49.2798 1130
 
0.4%
47.5609 1120
 
0.4%
50.8369 1107
 
0.4%
Other values (7863) 284147
96.2%
ValueCountFrequency (%)
0.0484 1
< 0.1%
0.0487 2
< 0.1%
0.0491 1
< 0.1%
0.0496 1
< 0.1%
0.0497 1
< 0.1%
0.0499 1
< 0.1%
0.0503 1
< 0.1%
0.0504 2
< 0.1%
0.0508 2
< 0.1%
0.0511 2
< 0.1%
ValueCountFrequency (%)
168.4491 1
 
< 0.1%
140.3037 1
 
< 0.1%
120.179 1
 
< 0.1%
118.6302 1
 
< 0.1%
116.8232 6
 
< 0.1%
115.3585 5
 
< 0.1%
113.6483 14
 
< 0.1%
112.2611 10
 
< 0.1%
110.6402 34
< 0.1%
109.3244 51
< 0.1%

R7 (MOhm)
Real number (ℝ)

Distinct7784
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.945815
Minimum0.0527
Maximum217.5961
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:55:59.337923image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0527
5-th percentile0.1222
Q11.8979
median31.8796
Q352.7077
95-th percentile80.5302
Maximum217.5961
Range217.5434
Interquartile range (IQR)50.8098

Descriptive statistics

Standard deviation27.697552
Coefficient of variation (CV)0.86701659
Kurtosis-1.0257914
Mean31.945815
Median Absolute Deviation (MAD)25.7326
Skewness0.36052466
Sum9440499.5
Variance767.15437
MonotonicityNot monotonic
2022-12-20T13:55:59.492718image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49.9924 1313
 
0.4%
49.1134 1310
 
0.4%
48.8606 1304
 
0.4%
53.0601 1299
 
0.4%
51.1767 1296
 
0.4%
50.8483 1289
 
0.4%
49.4202 1287
 
0.4%
52.0732 1262
 
0.4%
49.6787 1259
 
0.4%
47.778 1253
 
0.4%
Other values (7774) 282644
95.6%
ValueCountFrequency (%)
0.0527 1
< 0.1%
0.0531 1
< 0.1%
0.0536 2
< 0.1%
0.0539 1
< 0.1%
0.054 1
< 0.1%
0.0544 1
< 0.1%
0.0547 2
< 0.1%
0.0549 2
< 0.1%
0.0551 1
< 0.1%
0.0553 1
< 0.1%
ValueCountFrequency (%)
217.5961 1
 
< 0.1%
130.7453 1
 
< 0.1%
129.012 1
 
< 0.1%
116.9118 1
 
< 0.1%
115.5214 3
 
< 0.1%
113.8957 10
 
< 0.1%
112.5752 18
 
< 0.1%
111.0301 26
< 0.1%
109.7743 52
< 0.1%
108.304 63
< 0.1%

R8 (MOhm)
Real number (ℝ)

Distinct6186
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.939222
Minimum0.033
Maximum99.1944
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:55:59.781609image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.033
5-th percentile0.0996
Q111.8145
median27.0667
Q340.8277
95-th percentile60.1664
Maximum99.1944
Range99.1614
Interquartile range (IQR)29.0132

Descriptive statistics

Standard deviation19.590708
Coefficient of variation (CV)0.72721878
Kurtosis-0.83771284
Mean26.939222
Median Absolute Deviation (MAD)13.9908
Skewness0.14195499
Sum7960971.1
Variance383.79584
MonotonicityNot monotonic
2022-12-20T13:55:59.952994image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51.0146 1026
 
0.3%
53.0355 1022
 
0.3%
0.1003 1014
 
0.3%
54.073 1002
 
0.3%
54.8097 998
 
0.3%
50.6634 998
 
0.3%
0.1005 998
 
0.3%
46.5435 994
 
0.3%
0.1002 993
 
0.3%
0.1006 992
 
0.3%
Other values (6176) 285479
96.6%
ValueCountFrequency (%)
0.033 1
 
< 0.1%
0.0332 1
 
< 0.1%
0.0333 1
 
< 0.1%
0.0335 1
 
< 0.1%
0.0338 1
 
< 0.1%
0.0339 2
< 0.1%
0.0342 3
< 0.1%
0.0346 1
 
< 0.1%
0.0347 1
 
< 0.1%
0.0348 2
< 0.1%
ValueCountFrequency (%)
99.1944 1
 
< 0.1%
93.4149 1
 
< 0.1%
90.4024 1
 
< 0.1%
88.4405 1
 
< 0.1%
87.4055 3
 
< 0.1%
86.5611 5
 
< 0.1%
85.5689 4
 
< 0.1%
84.7592 7
 
< 0.1%
83.8072 12
< 0.1%
83.03 18
< 0.1%

R9 (MOhm)
Real number (ℝ)

Distinct6175
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.397564
Minimum0.0296
Maximum81.6883
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:56:00.124156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0296
5-th percentile0.097
Q18.6032
median21.8723
Q334.4416
95-th percentile51.7813
Maximum81.6883
Range81.6587
Interquartile range (IQR)25.8384

Descriptive statistics

Standard deviation16.872951
Coefficient of variation (CV)0.75333865
Kurtosis-0.81919077
Mean22.397564
Median Absolute Deviation (MAD)12.7287
Skewness0.25155591
Sum6618838.6
Variance284.69647
MonotonicityNot monotonic
2022-12-20T13:56:00.293178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0975 2819
 
1.0%
0.0976 2801
 
0.9%
0.0973 2796
 
0.9%
0.0972 2744
 
0.9%
0.0971 2649
 
0.9%
0.0977 2638
 
0.9%
0.097 2492
 
0.8%
0.0978 2391
 
0.8%
0.0968 2172
 
0.7%
0.098 2103
 
0.7%
Other values (6165) 269911
91.3%
ValueCountFrequency (%)
0.0296 1
 
< 0.1%
0.0298 2
 
< 0.1%
0.0301 1
 
< 0.1%
0.0302 2
 
< 0.1%
0.0303 2
 
< 0.1%
0.0304 2
 
< 0.1%
0.0306 2
 
< 0.1%
0.0307 2
 
< 0.1%
0.0308 1
 
< 0.1%
0.0309 6
< 0.1%
ValueCountFrequency (%)
81.6883 1
 
< 0.1%
71.8992 4
 
< 0.1%
70.1491 1
 
< 0.1%
69.5143 2
 
< 0.1%
68.9939 2
 
< 0.1%
68.3795 6
 
< 0.1%
67.8757 7
 
< 0.1%
67.2807 26
< 0.1%
66.7926 27
< 0.1%
66.2161 43
< 0.1%

R10 (MOhm)
Real number (ℝ)

Distinct6427
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.955787
Minimum0.0366
Maximum100.0304
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:56:00.529887image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0366
5-th percentile0.1179
Q17.5283
median23.7229
Q340.6416
95-th percentile63.7159
Maximum100.0304
Range99.9938
Interquartile range (IQR)33.1133

Descriptive statistics

Standard deviation20.847412
Coefficient of variation (CV)0.80318935
Kurtosis-0.77021057
Mean25.955787
Median Absolute Deviation (MAD)16.7097
Skewness0.41808492
Sum7670350.4
Variance434.61458
MonotonicityNot monotonic
2022-12-20T13:56:00.834011image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1197 2016
 
0.7%
0.1195 2007
 
0.7%
0.1198 1994
 
0.7%
0.1194 1979
 
0.7%
0.1191 1812
 
0.6%
0.1199 1797
 
0.6%
0.1193 1762
 
0.6%
0.1201 1659
 
0.6%
0.119 1594
 
0.5%
0.1202 1565
 
0.5%
Other values (6417) 277331
93.8%
ValueCountFrequency (%)
0.0366 1
 
< 0.1%
0.0373 1
 
< 0.1%
0.0374 1
 
< 0.1%
0.0377 1
 
< 0.1%
0.0379 3
< 0.1%
0.0383 1
 
< 0.1%
0.0384 1
 
< 0.1%
0.0385 2
< 0.1%
0.0387 1
 
< 0.1%
0.0388 1
 
< 0.1%
ValueCountFrequency (%)
100.0304 1
 
< 0.1%
98.8084 1
 
< 0.1%
94.5698 1
 
< 0.1%
89.8357 2
 
< 0.1%
88.8467 5
 
< 0.1%
88.0388 14
< 0.1%
87.0883 15
< 0.1%
86.3116 12
< 0.1%
85.3974 24
< 0.1%
84.6501 27
< 0.1%

R11 (MOhm)
Real number (ℝ)

Distinct6238
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.755315
Minimum0.0311
Maximum96.936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:56:01.040244image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0311
5-th percentile0.1078
Q110.5294
median27.2815
Q342.3694
95-th percentile63.4039
Maximum96.936
Range96.9049
Interquartile range (IQR)31.84

Descriptive statistics

Standard deviation20.766439
Coefficient of variation (CV)0.74819685
Kurtosis-0.83464589
Mean27.755315
Median Absolute Deviation (MAD)15.5086
Skewness0.2251464
Sum8202139.6
Variance431.24499
MonotonicityNot monotonic
2022-12-20T13:56:01.302047image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1082 2993
 
1.0%
0.1085 2937
 
1.0%
0.1086 2897
 
1.0%
0.1084 2870
 
1.0%
0.1081 2710
 
0.9%
0.1088 2524
 
0.9%
0.108 2332
 
0.8%
0.1078 2019
 
0.7%
0.1089 1936
 
0.7%
0.1077 1741
 
0.6%
Other values (6228) 270557
91.6%
ValueCountFrequency (%)
0.0311 1
< 0.1%
0.0312 1
< 0.1%
0.0317 2
< 0.1%
0.0318 1
< 0.1%
0.0321 1
< 0.1%
0.0323 2
< 0.1%
0.0324 2
< 0.1%
0.0325 1
< 0.1%
0.0326 2
< 0.1%
0.0327 2
< 0.1%
ValueCountFrequency (%)
96.936 2
 
< 0.1%
89.1159 2
 
< 0.1%
88.3056 4
 
< 0.1%
87.3522 18
 
< 0.1%
86.5731 12
 
< 0.1%
85.6562 16
 
< 0.1%
84.9067 24
< 0.1%
84.0241 33
< 0.1%
83.3024 38
< 0.1%
82.4524 55
< 0.1%

R12 (MOhm)
Real number (ℝ)

Distinct6251
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.772805
Minimum0.0327
Maximum129.9261
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:56:01.483677image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0327
5-th percentile0.1074
Q19.665
median25.9844
Q339.518
95-th percentile56.1889
Maximum129.9261
Range129.8934
Interquartile range (IQR)29.853

Descriptive statistics

Standard deviation18.897545
Coefficient of variation (CV)0.73323585
Kurtosis-0.82097155
Mean25.772805
Median Absolute Deviation (MAD)14.2727
Skewness0.15146467
Sum7616276.3
Variance357.1172
MonotonicityNot monotonic
2022-12-20T13:56:01.649770image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1087 1302
 
0.4%
0.1084 1292
 
0.4%
0.1086 1268
 
0.4%
0.1083 1234
 
0.4%
0.1088 1207
 
0.4%
0.1082 1171
 
0.4%
0.109 1151
 
0.4%
0.1091 1114
 
0.4%
0.108 1096
 
0.4%
0.1079 1060
 
0.4%
Other values (6241) 283621
96.0%
ValueCountFrequency (%)
0.0327 1
 
< 0.1%
0.0336 1
 
< 0.1%
0.0337 1
 
< 0.1%
0.0338 1
 
< 0.1%
0.034 1
 
< 0.1%
0.0341 3
< 0.1%
0.0342 3
< 0.1%
0.0343 5
< 0.1%
0.0344 2
 
< 0.1%
0.0346 1
 
< 0.1%
ValueCountFrequency (%)
129.9261 1
 
< 0.1%
85.8287 1
 
< 0.1%
85.0777 4
 
< 0.1%
84.1934 7
 
< 0.1%
83.4702 9
 
< 0.1%
82.6184 30
< 0.1%
81.9217 46
< 0.1%
81.1007 38
< 0.1%
80.4289 61
< 0.1%
79.6371 67
< 0.1%

R13 (MOhm)
Real number (ℝ)

Distinct6402
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.460428
Minimum0.0336
Maximum92.0217
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:56:01.848090image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0336
5-th percentile0.1008
Q17.7471
median21.4204
Q334.4882
95-th percentile52.8453
Maximum92.0217
Range91.9881
Interquartile range (IQR)26.7411

Descriptive statistics

Standard deviation17.276121
Coefficient of variation (CV)0.76918042
Kurtosis-0.71840386
Mean22.460428
Median Absolute Deviation (MAD)13.215
Skewness0.33141932
Sum6637415.9
Variance298.46437
MonotonicityNot monotonic
2022-12-20T13:56:02.042943image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1025 1217
 
0.4%
0.1026 1206
 
0.4%
0.1022 1175
 
0.4%
0.1023 1173
 
0.4%
0.1021 1165
 
0.4%
0.1019 1125
 
0.4%
0.1029 1118
 
0.4%
0.1027 1105
 
0.4%
0.103 1069
 
0.4%
0.1037 1059
 
0.4%
Other values (6392) 284104
96.1%
ValueCountFrequency (%)
0.0336 1
< 0.1%
0.0338 1
< 0.1%
0.0339 1
< 0.1%
0.0341 1
< 0.1%
0.0343 1
< 0.1%
0.0344 2
< 0.1%
0.0346 2
< 0.1%
0.0348 1
< 0.1%
0.0349 1
< 0.1%
0.0351 2
< 0.1%
ValueCountFrequency (%)
92.0217 1
 
< 0.1%
76.7411 1
 
< 0.1%
75.4135 4
 
< 0.1%
74.7083 3
 
< 0.1%
74.1305 3
 
< 0.1%
73.4487 5
 
< 0.1%
72.8899 6
 
< 0.1%
72.2303 12
< 0.1%
71.6896 22
< 0.1%
71.0511 24
< 0.1%

R14 (MOhm)
Real number (ℝ)

Distinct6206
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.84637
Minimum0.0316
Maximum118.8749
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-12-20T13:56:02.230107image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0316
5-th percentile0.1069
Q19.6501
median27.1416
Q345.0239
95-th percentile68.3837
Maximum118.8749
Range118.8433
Interquartile range (IQR)35.3738

Descriptive statistics

Standard deviation22.350827
Coefficient of variation (CV)0.77482286
Kurtosis-0.91558811
Mean28.84637
Median Absolute Deviation (MAD)17.6669
Skewness0.29974334
Sum8524563.8
Variance499.55946
MonotonicityNot monotonic
2022-12-20T13:56:02.559313image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1072 2675
 
0.9%
0.1074 2647
 
0.9%
0.1075 2579
 
0.9%
0.1077 2554
 
0.9%
0.1071 2520
 
0.9%
0.1079 2508
 
0.8%
0.1078 2474
 
0.8%
0.108 2300
 
0.8%
0.107 2275
 
0.8%
0.1069 1975
 
0.7%
Other values (6196) 271009
91.7%
ValueCountFrequency (%)
0.0316 1
 
< 0.1%
0.0321 2
 
< 0.1%
0.0322 2
 
< 0.1%
0.0324 2
 
< 0.1%
0.0326 3
< 0.1%
0.0327 3
< 0.1%
0.0329 3
< 0.1%
0.033 3
< 0.1%
0.0332 4
< 0.1%
0.0333 5
< 0.1%
ValueCountFrequency (%)
118.8749 1
 
< 0.1%
101.1097 1
 
< 0.1%
93.4235 1
 
< 0.1%
90.595 1
 
< 0.1%
88.7468 1
 
< 0.1%
87.7697 3
< 0.1%
86.9716 2
 
< 0.1%
86.0327 1
 
< 0.1%
85.2654 4
< 0.1%
84.3623 6
< 0.1%

Interactions

2022-12-20T13:55:48.279521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:30.535300image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:35.719392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:44.231359image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:48.396846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:52.002047image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:56.228317image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:59.756757image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:03.293259image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:07.366704image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:11.262388image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:15.157859image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:18.981813image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:22.761198image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:26.362561image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:30.035744image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:33.715066image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:37.442461image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:41.042424image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:44.658874image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:48.452280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:31.019844image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:35.959983image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:44.436840image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:48.560057image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:52.176721image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:56.401022image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:59.919761image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:03.455919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:07.537943image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:11.435266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:15.331760image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:19.152291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:22.921954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:26.527817image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:30.207945image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:33.906432image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:37.616828image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:41.219277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:44.816554image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:48.648159image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:31.315287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:36.226732image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:44.674954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:48.750663image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:52.380232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:56.586985image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:00.104060image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:03.646859image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:07.730262image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:11.645754image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:15.530517image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:19.350077image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:23.104300image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:26.720640image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:30.404941image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:34.103690image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:37.803384image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:41.403429image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:44.997090image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:48.824068image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:31.646036image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:36.436444image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:44.860112image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:48.926849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:52.638321image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:56.765440image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:00.275074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:03.816654image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:07.912335image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:11.842142image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:15.714665image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:19.526381image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:23.267534image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:26.894174image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:30.586555image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:34.287623image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:37.985134image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:41.688427image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:45.159595image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:48.997834image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:31.932759image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:39.899339image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:45.046440image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:49.093569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:52.847151image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:56.937539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:00.548287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:03.991025image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:08.088257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:12.022969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:15.932197image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:19.703757image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:23.430993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:27.064710image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:30.767200image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:34.469349image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:38.155711image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:41.845628image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:45.322871image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:49.177230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:32.294605image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:40.198901image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:45.264740image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:49.275509image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:53.035035image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:57.102093image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:00.738320image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:04.172778image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:08.283490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:12.208611image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:16.107980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:19.879349image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:23.597658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:27.239269image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:30.958834image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:34.655083image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:38.328053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:42.013292image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:45.487979image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:49.364504image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:32.533610image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:40.534912image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:45.478601image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:49.476286image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:53.242319image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:57.289547image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:00.916273image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:04.352668image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:08.469035image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:12.404588image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:16.310612image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:20.073826image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:23.897949image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:27.427045image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:31.159686image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:34.843228image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:38.515851image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:42.199561image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:45.720105image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:49.538179image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:32.746698image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:40.832837image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:45.668193image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:49.665302image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:53.440295image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:57.460067image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:01.083199image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:04.524427image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:08.649266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:12.699179image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:16.506294image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:20.261355image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:24.058700image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:27.612985image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:31.335513image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:35.013106image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:38.697294image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:42.375689image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:45.895242image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:49.716156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:32.914237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:41.201048image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:45.891398image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:49.857217image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2022-12-20T13:54:51.662040image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:55.874730image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:59.399853image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:02.950207image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:07.022694image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:10.894625image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:14.808300image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:18.621690image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:22.372551image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:26.026761image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:29.708736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:33.355749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:37.103124image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:40.662888image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:44.310738image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:47.946274image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:51.788337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:35.490718image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:44.018707image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:48.210524image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:51.827971image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:56.052583image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:54:59.568103image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:03.117330image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:07.187030image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:11.076592image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:14.982208image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:18.792161image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:22.580723image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:26.188097image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:29.859454image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:33.538355image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:37.266867image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:40.847287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:44.474661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-20T13:55:48.111133image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-12-20T13:56:02.733137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-12-20T13:56:03.070296image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-20T13:56:03.376637image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-20T13:56:03.676921image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-20T13:56:04.036519image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-20T13:55:52.080859image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-20T13:55:53.026143image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Time (s)CO (ppm)Humidity (%r.h.)Temperature (C)Flow rate (mL/min)Heater voltage (V)R1 (MOhm)R2 (MOhm)R3 (MOhm)R4 (MOhm)R5 (MOhm)R6 (MOhm)R7 (MOhm)R8 (MOhm)R9 (MOhm)R10 (MOhm)R11 (MOhm)R12 (MOhm)R13 (MOhm)R14 (MOhm)
00.0000.050.2526.54243.05380.88050.06430.14890.10560.10510.10930.13410.11510.11880.10620.12900.11830.12040.10870.1130
10.3090.050.2526.54242.00390.89280.07100.14360.10590.10130.10810.12850.11660.11030.10170.12230.11390.11660.10680.1110
20.6190.050.2526.54241.58100.89600.07620.14020.10800.10070.11000.12600.11880.10880.10090.12150.11280.11550.10670.1103
30.9280.050.2526.54241.15960.89800.07970.13920.11120.10110.11210.12520.12090.10860.10070.12120.11210.11520.10700.1102
41.2350.050.2526.54240.76420.89830.08220.13950.11420.10210.11430.12560.12330.10860.10040.12150.11200.11510.10720.1102
51.5450.050.2526.54240.76830.89900.08440.14040.11720.10320.11650.12620.12530.10860.10040.12150.11160.11510.10720.1100
61.8530.050.2526.54240.77230.89900.08630.14160.11990.10430.11850.12690.12730.10870.10030.12150.11130.11490.10740.1099
72.1610.050.2526.54240.77630.89900.08760.14320.12230.10530.12030.12770.12910.10860.10010.12150.11120.11490.10750.1099
82.4700.050.2526.54240.90380.89900.08890.14430.12420.10640.12200.12880.13060.10880.10000.12160.11090.11480.10780.1098
92.7790.050.2526.54241.05920.89900.09040.14550.12610.10730.12350.12960.13190.10860.10010.12150.11090.11480.10760.1099
Time (s)CO (ppm)Humidity (%r.h.)Temperature (C)Flow rate (mL/min)Heater voltage (V)R1 (MOhm)R2 (MOhm)R3 (MOhm)R4 (MOhm)R5 (MOhm)R6 (MOhm)R7 (MOhm)R8 (MOhm)R9 (MOhm)R10 (MOhm)R11 (MOhm)R12 (MOhm)R13 (MOhm)R14 (MOhm)
29550690906.8410.062.2826.420.54290.202060.944375.619482.203327.236670.107680.653182.883062.538546.833067.098064.335969.075451.245370.0976
29550790907.1460.062.2826.420.10370.202066.029679.068382.203327.033266.902273.507777.053158.888950.858370.957751.435763.531650.969568.3837
29550890907.4510.062.2826.420.00000.201067.504773.111174.582525.872263.973369.260270.814358.888948.738268.728364.855862.623949.790071.2524
29550990907.7570.062.2826.420.00000.200460.944368.557170.957725.327362.157267.015667.528060.659747.369366.083266.834962.623950.373069.4831
29551090908.0610.062.2826.420.00000.200054.460662.686968.144125.230655.741260.578261.782060.659748.171366.633065.830761.348450.642468.3837
29551190908.3660.062.2826.420.00000.200450.080958.078758.937421.788053.027754.919258.387056.777349.318463.212365.295459.235050.642467.8952
29551290908.6710.062.2826.420.00000.200046.118555.148356.972523.120250.562052.432654.023155.988050.858369.825564.335957.672550.053368.9791
29551390908.9760.062.2826.420.00000.200841.677548.704654.816122.905147.258450.495150.848356.777348.478970.335664.335958.443549.477568.9791
29551490909.2810.062.2826.420.00000.200038.154945.372749.520222.680846.249348.120649.992459.282448.478962.798764.855857.672549.220166.8445
29551590909.5850.062.2826.420.00000.200035.783941.459247.349821.788045.067246.239649.113456.777349.318461.040163.909056.921249.477568.3837